Factors influencing COVID-19 vaccination intention among parents of children aged 5-11 years in South Korea: a cross-sectional study.

PURPOSE: The purpose of this study was to identify factors affecting parents' intentions to have their children aged 5-11 years vaccinated against coronavirus disease 2019 (COVID-19). METHODS: The participants of the study were 298 parents with children aged 5-11 years in South Korea. Data collection took place from October 20 to October 26, 2022 and used an online survey (Google Forms). Data were analyzed using descriptive statistics, the t test, analysis of variance, the Scheffé test, Pearson correlation coefficients, and hierarchical regression in IBM SPSS version 26.0. RESULTS: The factors influencing participants' vaccination intentions for their children aged 5-11 years were cognitive behavioral control (ß=.40, p<.001), attitudes (ß=.37, p<.001), subjective norms (ß=.20, p<.001), and awareness of whether their child could receive the COVID-19 vaccine (ß=.07, p=.016). The explanatory power of the regression equation was 89%. CONCLUSION: Parents' intentions to vaccinate their children against COVID-19 are influenced by their attitudes, subjective norms, and perceived behavioral control toward vaccines. Since parents are concerned about vaccine side effects, it is important to establish a trusted line of communication to keep them informed about vaccinations.

Factors influencing the learning transfer of nursing students in a non-face-to-face educational environment during the COVID-19 pandemic in Korea: a cross-sectional study using structural equation modeling.

PURPOSE: The aim of this study was to identify factors influencing the learning transfer of nursing students in a non-face-to-face educational environment through structural equation modeling and suggest ways to improve the transfer of learning. METHODS: In this cross-sectional study, data were collected via online surveys from February 9 to March 1, 2022, from 218 nursing students in Korea. Learning transfer, learning immersion, learning satisfaction, learning efficacy, self-directed learning ability and information technology utilization ability were analyzed using IBM SPSS for Windows ver. 22.0 and AMOS ver. 22.0. RESULTS: The assessment of structural equation modeling showed adequate model fit, with normed χ2=1.74 (P<0.024), goodness-of-fit index=0.97, adjusted goodness-of-fit index=0.93, comparative fit index=0.98, root mean square residual=0.02, Tucker-Lewis index=0.97, normed fit index=0.96, and root mean square error of approximation=0.06. In a hypothetical model analysis, 9 out of 11 pathways of the hypothetical structural model for learning transfer in nursing students were statistically significant. Learning self-efficacy and learning immersion of nursing students directly affected learning transfer, and subjective information technology utilization ability, self-directed learning ability, and learning satisfaction were variables with indirect effects. The explanatory power of immersion, satisfaction, and self-efficacy for learning transfer was 44.4%. CONCLUSION: The assessment of structural equation modeling indicated an acceptable fit. It is necessary to improve the transfer of learning through the development of a self-directed program for learning ability improvement, including the use of information technology in nursing students' learning environment in non-face-to-face conditions.

Hyperspectral reflectance imaging for nondestructive evaluation of root rot in Korean ginseng (Panax ginseng Meyer).

Root rot of Panax ginseng caused by Cylindrocarpon destructans, a soil-borne fungus is typically diagnosed by frequently checking the ginseng plants or by evaluating soil pathogens in a farm, which is a time- and cost-intensive process. Because this disease causes huge economic losses to ginseng farmers, it is important to develop reliable and non-destructive techniques for early disease detection. In this study, we developed a non-destructive method for the early detection of root rot. For this, we used crop phenotyping and analyzed biochemical information collected using the HSI technique. Soil infected with root rot was divided into sterilized and infected groups and seeded with 1-year-old ginseng plants. HSI data were collected four times during weeks 7-10 after sowing. The spectral data were analyzed and the main wavelengths were extracted using partial least squares discriminant analysis. The average model accuracy was 84% in the visible/near-infrared region (29 main wavelengths) and 95% in the short-wave infrared (19 main wavelengths). These results indicated that root rot caused a decrease in nutrient absorption, leading to a decline in photosynthetic activity and the levels of carotenoids, starch, and sucrose. Wavelengths related to phenolic compounds can also be utilized for the early prediction of root rot. The technique presented in this study can be used for the early and timely detection of root rot in ginseng in a non-destructive manner.

Intra-person multi-task learning method for chronic-disease prediction.

In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases using the accumulated data of each patient. Thus, we propose an intra-person multi-task learning framework that jointly predicts the status of correlated chronic diseases and improves the model performance. Because chronic diseases occur over a long period and are affected by various factors, we considered features related to each chronic disease and the temporal relationship of the time-series data for accurate prediction. The study was carried out in three stages: (1) data preprocessing and feature selection using bidirectional recurrent imputation for time series (BRITS) and the least absolute shrinkage and selection operator (LASSO); (2) a convolutional neural network and long short-term memory (CNN-LSTM) for single-task models; and (3) a novel intra-person multi-task learning CNN-LSTM framework developed to predict multiple chronic diseases simultaneously. Our multi-task learning method between correlated chronic diseases produced a more stable and accurate system than single-task models and other baseline recurrent networks. Furthermore, the proposed model was tested using different time steps to illustrate its flexibility and generalization across multiple time steps.

Positive Effects on Emotional Stress and Sleep Quality of Forest Healing Program for Exhausted Medical Workers during the COVID-19 Outbreak.

This study targeted medical workers, who are currently being subjected to an excessive workload and emotional stress during the COVID-19 outbreak. Various treatment programs, such as a relaxation program to relieve stress, a walk in the forest, and woodworking were provided to the participants as forest healing therapies. We enrolled 13 medical workers (11 females, 2 males). Before and after forest healing therapy, stress and sleep-related questionnaires and levels of salivary cortisol, dehydroepiandrosterone sulfate (DHEA-S), and melatonin were measured and compared. The improvement of the perceived stress scale and the decrease of DHEA-S, a stress index, showed statistically significant results. However, although this study was conducted with a small number of participants and has a limitation in that the therapy occurred over a short period of only 1 night and 2 days, the trend of supporting results remains positive. As such, the authors propose forest healing therapy as one intervention to relieve the job stress for this group of workers.

High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image.

Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants' growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.

A Machine Learning Algorithm for Quantitatively Diagnosing Oxidative Stress Risks in Healthy Adult Individuals Based on Health Space Methodology: A Proof-of-Concept Study Using Korean Cross-Sectional Cohort Data.

Oxidative stress aggravates the progression of lifestyle-related chronic diseases. However, knowledge and practices that enable quantifying oxidative stress are still lacking. Here, we performed a proof-of-concept study to predict the oxidative stress status in a healthy population using retrospective cohort data from Boramae medical center in Korea (n = 1328). To obtain binary performance measures, we selected healthy controls versus oxidative disease cases based on the "health space" statistical methodology. We then developed a machine learning algorithm for discrimination of oxidative stress status using least absolute shrinkage and selection operator (LASSO)/elastic net regression with 10-fold cross-validation. A proposed fine-tune model included 16 features out of the full spectrum of diverse and complex data. The predictive performance was externally evaluated by generating receiver operating characteristic curves with area under the curve of 0.949 (CI 0.925 to 0.974), sensitivity of 0.923 (CI 0.879 to 0.967), and specificity of 0.855 (CI 0.795 to 0.915). Moreover, the discrimination power was confirmed by applying the proposed diagnostic model to the full dataset consisting of subjects with various degrees of oxidative stress. The results provide a feasible approach for stratifying the oxidative stress risks in the healthy population and selecting appropriate strategies for individual subjects toward implementing data-driven precision nutrition.

Phenological growth stages of Korean ginseng (Panax ginseng) according to the extended BBCH scale.

BACKGROUND: Phenological studies are a prerequisite for accomplishing higher productivity and better crop quality in cultivated plants. However, there are no phenological studies on Panax ginseng that improve its production yield. This study aims to redefine the phenological growth stages of P. ginseng based on the existing Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale and proposes a disease control reference. METHODS: This study was conducted at the Korea Ginseng Corporation Experiment Station in Gyeonggi province, South Korea. Phenological observations were performed once weekly or twice monthly, based on the developmental stages. The existing BBCH scale with a three-digit code was used to redefine and update P. ginseng's phenological growth codes. RESULTS: The phenological description is divided into eight principal growth stages: three for vegetative growth (perennating bud, aerial shoot, and root development), four for reproductive growth (reproductive organ development, flowering, fruit development, and fruit maturation), and one for senescence according to the extended BBCH scale. A total of 58 secondary growth stages were described within the eight principal growth stages. Under each secondary growth stage, four mesostages are also taken into account, which contains the distinct patterns of the phenological characteristics in ginseng varieties and the process of transplanting seedlings. A practical management program for disease control was also proposed by using the BBCH code and the phenological data proposed in this work. CONCLUSION: The study introduces an extended BBCH scale for the phenological research of P. ginseng.

Metabolic Profiling Analysis Reveals the Potential Contribution of Barley Sprouts against Oxidative Stress and Related Liver Cell Damage in Habitual Alcohol Drinkers.

Chronic excessive alcohol consumption is associated with multiple liver defects, such as steatosis and cirrhosis, mainly attributable to excessive reactive oxygen species (ROS) production. Barley sprouts (Hordeum vulgare L.) contain high levels of polyphenols that may serve as potential antioxidants. This study aimed to investigate whether barley sprouts extract powder (BSE) relieves alcohol-induced oxidative stress and related hepatic damages in habitual alcohol drinkers with fatty liver. In a 12-week randomized controlled trial with two arms (placebo or 480 mg/day BSE; n = 76), we measured clinical markers and metabolites at the baseline and endpoint to understand the complex molecular mechanisms. BSE supplementation reduced the magnitude of ROS generation and lipid peroxidation and improved the glutathione antioxidant system. Subsequent metabolomic analysis identified alterations in glutathione metabolism, amino acid metabolism, and fatty acid synthesis pathways, confirming the role of BSE in glutathione-related lipid metabolism. Finally, the unsupervised machine learning algorithm indicated that subjects with lower glutathione reductase at the baseline were responders for liver fat content, and those with higher fatigue and lipid oxidation were responders for γ-glutamyl transferase. These findings suggest that BSE administration may protect against hepatic injury by reducing oxidative stress and changing the metabolism in habitual alcohol drinkers with fatty liver.

Needs and expectations for an AR program for asthma education for school-age children in South Korea: The perspectives of children, parents, and teachers.

PURPOSE: This study examined the needs for asthma education programs as perceived by school-age children, parents, and teachers and investigated parents' and teachers' expectations for incorporating augmented reality (AR) in asthma education. METHODS: This descriptive, cross-sectional study included 339 participants: 125 school-age children, 132 parents, and 82 teachers. Data were collected from children, parents, and teachers on the need for asthma education, the requirements for asthma education, and the expectations of parents and teachers for incorporating AR in asthma education. Data were analyzed using descriptive statistics, the t-test, analysis of variance, and the Scheffé test. RESULTS: Asthmatic children and their peers, parents, and teachers all felt there was a significant need for education about asthma, with education on how to deal with an asthma attack being needed the most. The incorporation of AR programs in asthma education was viewed positively by both parents and teachers. CONCLUSION: An AR children's asthma education program should be developed in which children with asthma and their peers, parents, and teachers can participate together. Furthermore, it is expected that children with asthma will independently undertake more effective disease management after attending an AR asthma education program.

ClimActor, harmonized transnational data on climate network participation by city and regional governments.

Cities and regions have become increasingly engaged in global climate change governance. They are pledging their own climate mitigation targets and participating in membership networks that typically are transnational in nature and engage thousands of subnational governments. Researching these growing trends in participation has been difficult due to the disparate and inconsistent nature of this self-reported data. To facilitate future analyses of these actors, we introduce ClimActor, the largest harmonized global dataset of more than 10,000 city and regional governments participating in networks like the Global Covenant of Mayors for Climate and Energy, C40 Cities for Climate Leadership, ICLEI Local Leaders for Sustainability, among others. We include key contextual information on each actor's population, geographic location, and administrative jurisdiction to facilitate disambiguation of potential overlaps in actions or emissions. We also provide a series of cleaning functions based on phonetic and fuzzy string matching algorithms within an open-source R package to make it easy for anyone to immediately use the ClimActor dataset with other relevant data.

The Structural Equation Model of Intention to Discontinue Drinking Highly Caffeinated Beverage of Undergraduate Students.

PURPOSE: The purpose of this study was to test a model for intention to discontinuation drinking high caffeinated beverages among undergraduate students. This model was based on the Ajzen's theory of planned behavior and Becker's health belief model. METHODS: Participants consisted of 201 undergraduate students. Data were collected by questionnaires from March 11 to May 24, 2019. Collected data were analyzed using SPSS/WIN 22.0, AMOS 22.0 program. RESULTS: The assessment of the model indicated an acceptable fit (normed χ 2=1.65, goodness-of-fit index [GFI]=.83, adjusted GFI=.79, comparative fit index [CFI]=.92, standardized root mean square residual [SRMR]=.05, Tucker-Lewis index [TLI]=.91, normed fit index [NFI]=.87, root mean square error of approximation [RMSEA]=.07). Perceived behavior control, subjective norm, the subjective attitude was found to have a significant direct effect on the intention to discontinuation of drinking a high caffeinated beverage. The variances of this model explained 45.3% of the variance in intention to discontinuation of drinking a high caffeinated beverage. CONCLUSION: These results suggest that a need to increase awareness of adverse effects and potential risks of high caffeinated beverage consumption in undergraduate students. Besides, the university and government should provide education and campaigns to prevent excessive high-caffeinated beverage consumption.

A tRNA- and Anticodon-Centric View of the Evolution of Aminoacyl-tRNA Synthetases, tRNAomes, and the Genetic Code.

Pathways of standard genetic code evolution remain conserved and apparent, particularly upon analysis of aminoacyl-tRNA synthetase (aaRS) lineages. Despite having incompatible active site folds, class I and class II aaRS are homologs by sequence. Specifically, structural class IA aaRS enzymes derive from class IIA aaRS enzymes by in-frame extension of the protein N-terminus and by an alternate fold nucleated by the N-terminal extension. The divergence of aaRS enzymes in the class I and class II clades was analyzed using the Phyre2 protein fold recognition server. The class I aaRS radiated from the class IA enzymes, and the class II aaRS radiated from the class IIA enzymes. The radiations of aaRS enzymes bolster the coevolution theory for evolution of the amino acids, tRNAomes, the genetic code, and aaRS enzymes and support a tRNA anticodon-centric perspective. We posit that second- and third-position tRNA anticodon sequence preference (C>(U~G)>A) powerfully selected the sectoring pathway for the code. GlyRS-IIA appears to have been the primordial aaRS from which all aaRS enzymes evolved, and glycine appears to have been the primordial amino acid around which the genetic code evolved.

Type-II tRNAs and Evolution of Translation Systems and the Genetic Code.

Because tRNA is the core biological intellectual property that was necessary to evolve translation systems, tRNAomes, ribosomes, aminoacyl-tRNA synthetases, and the genetic code, the evolution of tRNA is the core story in evolution of life on earth. We have previously described the evolution of type-I tRNAs. Here, we use the same model to describe the evolution of type-II tRNAs, with expanded V loops. The models are strongly supported by inspection of typical tRNA diagrams, measuring lengths of V loop expansions, and analyzing the homology of V loop sequences to tRNA acceptor stems. Models for tRNA evolution provide a pathway for the inanimate-to-animate transition and for the evolution of translation systems, the genetic code, and cellular life.

Aminoacyl-tRNA synthetase evolution and sectoring of the genetic code.

The genetic code sectored via tRNA charging errors, and the code progressed toward closure and universality because of evolution of aminoacyl-tRNA synthetase (aaRS) fidelity and translational fidelity mechanisms. Class I and class II aaRS folds are identified as homologs. From sequence alignments, a structurally conserved Zn-binding domain common to class I and class II aaRS was identified. A model for the class I and class II aaRS alternate folding pathways is posited. Five mechanisms toward code closure are highlighted: 1) aaRS proofreading to remove mischarged amino acids from tRNA; 2) accurate aaRS active site specification of amino acid substrates; 3) aaRS-tRNA anticodon recognition; 4) conformational coupling proofreading of the anticodon-codon interaction; and 5) deamination of tRNA wobble adenine to inosine. In tRNA anticodons there is strong wobble sequence preference that results in a broader spectrum of contacts to synonymous mRNA codon wobble bases. Adenine is excluded from the anticodon wobble position of tRNA unless it is modified to inosine. Uracil is generally preferred to cytosine in the tRNA anticodon wobble position. Because of wobble ambiguity when tRNA reads mRNA, the maximal coding capacity of the three nucleotide code read by tRNA is 31 amino acids + stops.

[Structural Equation Model of Health-Related Quality of Life in School Age Children with Asthma].

PURPOSE: This study aimed to construct and test a hypothetical model of the quality of life of school-age children with asthma based on the health-related quality of life model by Wilson and Cleary. METHODS: Data were collected from 205 pairs of pediatric outpatients diagnosed with asthma and their parents in Seoul and Gyeonggi-do from July 2016 to April 2017. The exogenous variables were asthma knowledge, number of accompanying allergic diseases, and social support. The endogenous variables were asthma self-efficacy, asthma symptom control, perceived health status, parental quality of life, and children's quality of life. For data analysis, descriptive statistics, factor analysis, and structural equation modeling were performed. RESULTS: Eighteen of the twenty-four hypotheses selected for the hypothetical model were attentive and supported statistically. Quality of life was explained by asthma self-efficacy, asthma symptom control, perceived health, parental quality of life, and asthma knowledge with 83.5%. CONCLUSION: Strategies for promoting self-efficacy and enforcing asthma knowledge will be helpful for the improvement of health-related quality of life with school-aged asthmatic children.

Homology threading to generate RNA polymerase structures.

Homology threading is a powerful technology for generating structural models based on homologous structures. Here we use threading to generate four complex RNA polymerase models. The models appear to be as useful as x-ray crystal structures or cryo-electron microscopy structures to support research projects.